Transfer Learning to Accelerate Interface Structure Searches

Interfaces have atomic structures that are significantly different from those in the bulk, and play crucial roles in material properties. The central structures at the interfaces that provide properties have been extensively investigated. However, determination of even one interface structure requires searching for the stable configuration among many thousands of candidates. Here, a powerful combination of machine learning techniques based on kriging and transfer learning (TL) is proposed as a method for unveiling the interface structures. Using the kriging+TL method, thirty-three grain boundaries were systematically determined from 1,650,660 candidates in only 462 calculations, representing an increase in efficiency over conventional all-candidate calculation methods, by a factor of approximately 3,600.